[Paper Review] An Optimized Huffmans Coding by the method of Grouping

Rupesh Gautam, S. Murali|arXiv (Cornell University)|Jul 28, 2016

Algorithms and Data Compression1 references3 citations

TL;DR

This paper proposes an optimized Huffman coding method that groups characters to reduce data size and tree complexity, achieving better compression efficiency than standard Huffman coding by minimizing both encoded data and decoding tree overhead. The approach improves processing speed and scalability for large-scale data applications such as machine learning and IoT.

ABSTRACT

Data compression has become a necessity not only the in the field of communication but also in various scientific experiments. The data that is being received is more and the processing time required has also become more. A significant change in the algorithms will help to optimize the processing speed. With the invention of Technologies like IoT and in technologies like Machine Learning there is a need to compress data. For example training an Artificial Neural Network requires a lot of data that should be processed and trained in small interval of time for which compression will be very helpful. There is a need to process the data faster and quicker. In this paper we present a method that reduces the data size. This method is known as Optimized Huffmans Coding. In the Huffmans coding we encode the messages so as to reduce the data and here in the optimized Huffmans coding we compress the data to a great extent which helps in various signal processing algorithms and has advantages in many applications. Here in this paper we have presented the Optimized Huffmans Coding method for Text compression. This method but has advantages over the normal Huffmans coding. This algorithm presented here says that every letter can be grouped together and encoded which not only reduces the size but also the Huffmans Tree data that is required for decoding, hence reducing the data size. This method has huge scientific applications.

Motivation & Objective

• Address the growing need for faster data compression in data-intensive fields like machine learning and IoT.
• Reduce the computational and storage overhead of Huffman coding by minimizing the size of the Huffman decoding tree.
• Improve compression efficiency beyond standard Huffman coding through intelligent grouping of characters.
• Enable faster processing of large datasets by reducing both encoded data size and decoding complexity.
• Provide a scalable solution for text compression in scientific and real-time data processing systems.

Proposed method

• Group characters into clusters based on frequency or other criteria to form composite symbols.
• Construct a Huffman tree for the grouped symbols instead of individual characters, reducing the number of leaf nodes.
• Encode each group as a single unit using standard Huffman encoding principles, thereby reducing the total number of bits.
• Optimize the grouping strategy to minimize the expected code length while maintaining decoding feasibility.
• Reduce the size of the Huffman tree by decreasing the number of nodes, which lowers storage and transmission costs.
• Apply the method specifically to text data, demonstrating its effectiveness in reducing overall data footprint.

Experimental results

Research questions

• RQ1 Can grouping characters in Huffman coding reduce the size of the encoded data more effectively than standard Huffman coding?
• RQ2 Does reducing the number of nodes in the Huffman tree through grouping lead to lower storage and decoding overhead?
• RQ3 How does the proposed grouping method compare to traditional Huffman coding in terms of compression ratio and processing speed?
• RQ4 What is the optimal strategy for grouping characters to maximize compression efficiency without increasing decoding complexity?
• RQ5 To what extent can this method improve performance in data-intensive applications like machine learning and IoT?

Key findings

• The proposed method achieves higher compression ratios than standard Huffman coding by reducing the number of symbols to be encoded.
• Grouping reduces the size of the Huffman tree, which decreases storage and transmission costs for the decoding structure.
• The method significantly reduces processing time by minimizing the number of encoding and decoding operations.
• The algorithm is particularly effective for large text datasets, where the benefits of grouping are amplified.
• The approach maintains lossless compression while improving efficiency, making it suitable for real-time and high-throughput applications.
• The results demonstrate that grouping characters before Huffman encoding leads to a measurable reduction in both data size and tree complexity.